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MicroBayes

Probabilistic Machines for Low-level Sensor Interpretation

Position

Scientific Context

The development of modern computers is mainly based on increase of performances and decrease of size and energy consumption. This incremental evolution is notable, but it involves no notable modification of the basic principles of computation. In particular, all the components perform deterministic and exact operations on sets of binary signals. These constraints obviously impede further sizable progresses in terms of speed, miniaturization and power consumption. As detailed below, the goal of the MicroBayes project is twofold:

to investigate a radically different approach to perform computations, namely stochastic computing using stochastic bit streams.

to show that stochastic architectures can outperform standard computers to solve complex inference problems both in terms of execution speed and of power consumption.

We will evaluate stochastic machines on difficult Bayesian inference problems. Moreover we will demonstrate the interest and feasibility of stochastic computing on two applications involving low-level information processing from sensor signals. The given application are sound source localization and separation as shown below: